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Article: Roadmap on emerging hardware and technology for machine learning

TitleRoadmap on emerging hardware and technology for machine learning
Authors
Keywordsartificial intelligence
machine learning
neural network models
neuromorphic computing
hardware technologies
Issue Date2020
PublisherInstitute of Physics Publishing. The Journal's web site is located at http://www.iop.org/journals/nano
Citation
Nanotechnology, 2020, v. 32 n. 1, p. article no. 012002 How to Cite?
AbstractRecent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.
DescriptionHybrid open access
Persistent Identifierhttp://hdl.handle.net/10722/295356
ISSN
2023 Impact Factor: 2.9
2023 SCImago Journal Rankings: 0.631
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorBerggren, K-
dc.contributor.authorXia, Q-
dc.contributor.authorLikharev, KK-
dc.contributor.authorStrukov, DB-
dc.contributor.authorJiang, H-
dc.contributor.authorMikolajick, T-
dc.contributor.authorQuerlioz, D-
dc.contributor.authorSalinga, M-
dc.contributor.authorErickson, JR-
dc.contributor.authorPi, S-
dc.contributor.authorXiong, F-
dc.contributor.authorLin, P-
dc.contributor.authorLi, C-
dc.contributor.authorChen, Y-
dc.contributor.authorXiong, S-
dc.contributor.authorHoskins, BD-
dc.contributor.authorDaniels, MW-
dc.contributor.authorMadhavan, A-
dc.contributor.authorLiddle, JA-
dc.contributor.authorMcClelland, JJ-
dc.contributor.authorYang, Y-
dc.contributor.authorRupp, J-
dc.contributor.authorNonnenmann, SS-
dc.contributor.authorCheng, KT-
dc.contributor.authorGong, N-
dc.contributor.authorLastras-Montaño, MA-
dc.contributor.authorTalin, AA-
dc.contributor.authorSalleo, A-
dc.contributor.authorShastri, BJ-
dc.contributor.authorde Lima, TF-
dc.contributor.authorPrucnal, P-
dc.contributor.authorTait, AN-
dc.contributor.authorShen, Y-
dc.contributor.authorMeng, H-
dc.contributor.authorRoques-Carmes, C-
dc.contributor.authorCheng, Z-
dc.contributor.authorBhaskaran, H-
dc.contributor.authorJariwala, D-
dc.contributor.authorWang, H-
dc.contributor.authorShainline, JM-
dc.contributor.authorSegall, K-
dc.contributor.authorYang, JJ-
dc.contributor.authorRoy, K-
dc.contributor.authorDatta, S-
dc.contributor.authorRaychowdhury, A-
dc.date.accessioned2021-01-11T13:58:56Z-
dc.date.available2021-01-11T13:58:56Z-
dc.date.issued2020-
dc.identifier.citationNanotechnology, 2020, v. 32 n. 1, p. article no. 012002-
dc.identifier.issn0957-4484-
dc.identifier.urihttp://hdl.handle.net/10722/295356-
dc.descriptionHybrid open access-
dc.description.abstractRecent progress in artificial intelligence is largely attributed to the rapid development of machine learning, especially in the algorithm and neural network models. However, it is the performance of the hardware, in particular the energy efficiency of a computing system that sets the fundamental limit of the capability of machine learning. Data-centric computing requires a revolution in hardware systems, since traditional digital computers based on transistors and the von Neumann architecture were not purposely designed for neuromorphic computing. A hardware platform based on emerging devices and new architecture is the hope for future computing with dramatically improved throughput and energy efficiency. Building such a system, nevertheless, faces a number of challenges, ranging from materials selection, device optimization, circuit fabrication and system integration, to name a few. The aim of this Roadmap is to present a snapshot of emerging hardware technologies that are potentially beneficial for machine learning, providing the Nanotechnology readers with a perspective of challenges and opportunities in this burgeoning field.-
dc.languageeng-
dc.publisherInstitute of Physics Publishing. The Journal's web site is located at http://www.iop.org/journals/nano-
dc.relation.ispartofNanotechnology-
dc.rightsNanotechnology. Copyright © Institute of Physics Publishing.-
dc.rightsThis is an author-created, un-copyedited version of an article published in [insert name of journal]. IOP Publishing Ltd is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/[insert DOI].-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectartificial intelligence-
dc.subjectmachine learning-
dc.subjectneural network models-
dc.subjectneuromorphic computing-
dc.subjecthardware technologies-
dc.titleRoadmap on emerging hardware and technology for machine learning-
dc.typeArticle-
dc.identifier.emailLi, C: canl@hku.hk-
dc.identifier.authorityLi, C=rp02706-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1088/1361-6528/aba70f-
dc.identifier.pmid32679577-
dc.identifier.scopuseid_2-s2.0-85094219964-
dc.identifier.hkuros320857-
dc.identifier.volume32-
dc.identifier.issue1-
dc.identifier.spagearticle no. 012002-
dc.identifier.epagearticle no. 012002-
dc.identifier.isiWOS:000579646000001-
dc.publisher.placeUnited Kingdom-

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